Weekly Exercises #4

library(tidyverse)     # for data cleaning and plotting
library(googlesheets4) # for reading googlesheet data
library(lubridate)     # for date manipulation
library(openintro)     # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data
library(ggmap)         # for mapping points on maps
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(ggthemes)      # for more themes (including theme_map())
gs4_deauth()           # To not have to authorize each time you knit.
theme_set(theme_minimal())

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 

# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
  place = c("Home", "Macalester College", "Adams Spanish Immersion", 
            "Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
            "Dance Spectrum", "Pizza Luce", "Brunson's"),
  long = c(-93.1405743, -93.1712321, -93.1451796, 
           -93.1650563, -93.1542883, -93.1696608, 
           -93.1393172, -93.1524256, -93.0753863),
  lat = c(44.950576, 44.9378965, 44.9237914,
          44.9654609, 44.9295072, 44.9436813, 
          44.9399922, 44.9468848, 44.9700727)
  )
favorite_stp_by_lisa

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

Warm-up exercises from tutorial

These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?

ggplot(data=Starbucks) +
  geom_point(aes(x = Longitude, y = Latitude, color = `Ownership Type`), 
             alpha = 0.2, 
             size = .5)

### It is hard to deduce anything because if you make the points too large you loose out on the detail. Generally We see that the majority of Licensed and Company owned Starbucks are in North America. East Asia, particularly along the south eastern coast of China has a lot of joint venture ownership, whilst the UK has a cluster of franchise. THere are not many starbucks in South America, Africa and Oceana.

2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

minnesota <- get_stamenmap(
    bbox = c(left = -93.67, bottom = 44.75, right = -92.59, top = 45.18), 
    maptype = "terrain",
    zoom = 11)


ggmap(minnesota) +
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude), 
             size = 2)

3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map).

The zoom number shows the amount of detail the map will show. Small zoom number have less detail and large zoom numbers have more detail.

4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

minnesota <- get_stamenmap(
    bbox = c(left = -93.67, bottom = 44.75, right = -92.59, top = 45.18), 
    maptype = "watercolor",
    zoom = 11)

ggmap(minnesota) + 
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude, color = `Ownership Type`), 
             size = 3) +
  theme_map() +
  theme(legend.background = element_blank())

5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do think, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

ggmap(minnesota) + 
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude, color = `Ownership Type`), 
             size = 3) +
  theme_map() +
  theme(legend.background = element_blank()) + 
  annotate(geom = "text", x = -93.1712321, y = 44.9308890, label = "Macalester College") +
  annotate(geom = "point", x = -93.1712321, y = 44.9378965, color = "Dark Red", size = 3)

Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

starbucks_with_2018_pop_est <-
  starbucks_us_by_state %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% 
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)

6. dplyr review: Look through the code above and describe what each line of code does.

census_pop_est_2018: This line reads the data table as a cvs creating the data set. The lines below it feed into the creation through the pipe. First we separate the dot (period) and state from eachother so that the state object is simply a character. The merge then ensures that states with two words are still counted as one object. The select line removes the dot from the variables and finally we create change the state variable to only be in lower case.

Next we are creating a new data set. We first assign the data set the name starbucks_with_2018_pop_est, then we begin the join of the starbucks by state data set and the 2018 census population data set we just created. Due to the nature of the join we will only be keeping objects that we are favoring the starbucks by state doc to dictate the number of objects we have. the join is done by equating state name to state as a common variable. finally we create a variable that divides the total number of Starbucks in a state by the estimated population number in 2018, and then multiplies that number by 10,000.

7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.

A few of your favorite things (leaflet)

map_by_state <- map_data("state")

starbucks_with_2018_pop_est %>% 
  ggplot() +
  geom_map(map = map_by_state,
           aes(map_id = state_name,
               fill = starbucks_per_10000)) +
  geom_point(data = Starbucks %>% 
               filter(!`State/Province` %in% c("HI", "AK"), `Country` == "US"),
             aes(x = Longitude, y = Latitude),
             size = .05,
             alpha = .2, 
             color = "goldenrod") +
  expand_limits(x = map_by_state$long, y = map_by_state$lat) + 
  labs(title = "A Chloropleth Map of Starbucks in the United States") +
  theme(legend.background = element_blank()) + 
  theme_map() +
  scale_fill_viridis_c()

### there starbucks is more common in the east and west cost than it is in the upper midwest. The number of starbucks also follows urban population nodes. Places with a larger population, or higher population density, have more starbucks. Look at New York city or the twin cities

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.

likhwas_favorite_places <- tibble(
  place = c("Macalester College", "Home", "MyBurger", 
            "Scotts", "Bdubs", "Hamberguesas El Gordo", 
            "Pimento", "Osceola", "Highland Golfcourse", 
            "Mattocks Park", "Shadow Falls Park"),
    long = c(-93.173561, -93.169294,-93.166664,
           -93.162217, -93.166106, -93.136658, 
           -93.124741, -93.160227, -93.165663,
           -93.170888, -93.196246),
  lat = c(44.940094, 44.94719, 44.939892,
          44.933941, 44.943719, 44.946293, 
          44.92878, 44.936311, 44.919263, 
          44.928372 , 44.942555),
  favorite = c("yes", "yes", "no", 
               "no", "no", "no", 
               "no", "no","yes",
               "no","no")
)

pal <- colorFactor(
                    palette = c("#333399", "#660033"), 
                   domain = likhwas_favorite_places$favorite)

leaflet(data = likhwas_favorite_places) %>% 
          addProviderTiles(providers$CartoDB.DarkMatter) %>% 
          addTiles() %>% 
          addCircles(lat = ~lat,
                     lng = ~long,
                     label = ~favorite, 
                     weight = 10,
                     opacity = 1,
                     color = ~pal(favorite)) %>% 
          addPolylines(lat = ~lat,
                       lng = ~long, 
                       color = col2hex("darkgreen")) %>% 
          addLegend(pal = pal,
                    values = ~favorite, 
                    opacity = 1, 
                    title = "Favorite Place",
                    position = "bottomright")

• Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

• If there are other variables you want to add that could enhance your plot, do that now.

Revisiting old datasets

This section will revisit some datasets we have used previously and bring in a mapping component.

Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

new_stations <- Stations %>%
  left_join(Trips,
  by = c("name" = "sstation")) %>%
  group_by(long, lat) %>% 
  summarize(total_departures = n())


Washington_DC <- get_stamenmap(
    bbox = c(left = -77.1436, bottom = 38.8253, right = -76.9297, top = 38.9738), 
    maptype = "toner-hybrid",
    zoom = 12)

ggmap(Washington_DC) +
  geom_point(data = new_stations,
             aes(x = long, y = lat, color = total_departures), 
             size = 2) +
  theme_map() +
  theme(legend.background = element_blank())

10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are.

new_stations <- Stations %>%
  left_join(Trips,
  by = c("name" = "sstation")) %>%
  group_by(long, lat) %>% 
  summarize(percent_casual= mean(client == "Casual"))


Washington_DC <- get_stamenmap(
    bbox = c(left = -77.1436, bottom = 38.8253, right = -76.9297, top = 38.9738), 
    maptype = "toner-lite",
    zoom = 12)

ggmap(Washington_DC) +
  geom_point(data = new_stations,
             aes(x = long, y = lat, color = percent_casual), 
             size = 2) +
  theme_map() +
  theme(legend.background = element_blank())

### There is a high number of casual riders that rent bikes from the city center and closer to the river. People further away are most likely using the bikes to commute or to get home while closer to the river riders would be renting for leisure.

COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map?

states_map <- map_data("state")

new_covid19 <-covid19 %>% 
  group_by(state) %>% 
  summarise(cumulative_cases = max(cases)) %>%
  mutate(state = str_to_lower(state)) 

new_covid19 %>% 
  ggplot() +
  geom_map(map = states_map,
        aes(map_id = state, 
            fill = cumulative_cases)) +
  expand_limits(x = states_map$long, 
                y = states_map$lat) + 
  theme_map() +
  labs( title = "Cumulative Covid19 Cases in the United States of America",
        fill = "Cumulative Cases") +
  scale_fill_viridis_c() 

### The map shows the cumulative cases however this is biased towards population size of the state. Texas and California have the highest populations in the US and therefore the most cases.

12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

states_map <- map_data("state")

new_covid19 <-covid19 %>% 
  group_by(state) %>% 
  summarise(cumulative_cases = max(cases)) %>%
  mutate(state = str_to_lower(state)) 

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))



covid_with2018pop <- 
  new_covid19 %>% 
  left_join(census_pop_est_2018,
            by = "state") %>% 
  mutate(cases_per10000 = (cumulative_cases/est_pop_2018)* 10000)

covid_with2018pop %>% 
  ggplot() +
  geom_map(map = states_map,
        aes(map_id = state, 
            fill = cases_per10000)) +
  expand_limits(x = states_map$long, 
                y = states_map$lat) + 
  theme_map() +
  labs( title = "Covid19 Cases in the United States of America",
        fill = "Cases per 10,000 people") +
  scale_fill_viridis_c() 

13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?

states_map <- map_data("state")

date_covid19 <- covid19 %>% 
  filter(date %in% ymd(c( "2020-03-18", "2020-05-18", "2020-07-18", "2020-09-18"))) %>% 
  group_by(state) %>% 
  mutate(state = str_to_lower(state)) %>% 
  left_join(census_pop_est_2018,
            by = "state") %>% 
  mutate(cases_per10000 = (cases/est_pop_2018)* 10000) 


date_covid19 %>% 
  ggplot() +
  geom_map(map = states_map,
        aes(map_id = state, 
            fill = cases_per10000)) +
  expand_limits(x = states_map$long, 
                y = states_map$lat) + 
  facet_wrap(vars(date)) +
  labs( title = "Covid19 Cases in the United States of America",
        fill = "Cases per 10,000 people") + 
  theme_map() +
  theme(legend.background = element_blank(),
        legend.direction = "horizontal",
        legend.position = "bottom")

### In these four graphs we see that over time the cases of Covid19 have increased. In the most recent data the south eastern states, like florida and louisianna, have the highest cases. THe data, however, does not show testing dates. Some states began testing later than others which led to lower rates.

Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

mpls_suspicious <- MplsStops %>%
  group_by(neighborhood, problem) %>% 
  summarize(neighborhood_stops = n()) %>% 
  mutate(proportion_sus = neighborhood_stops/sum(neighborhood_stops)) %>% 
  arrange(desc(neighborhood_stops)) %>% 
  filter(problem =="suspicious")
mpls_suspicious

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.

pal <- colorFactor(
                    palette = c("#333399", "#660033"), 
                   domain = MplsStops$problem)

leaflet(data = MplsStops) %>% 
  addTiles() %>%
  addCircles(lat = ~ lat,
             lng = ~ long,
             weight = 1,
             label = ~ problem,
             color = ~pal(problem)) %>% 
  addLegend(position = "bottomright",
            pal = pal,
            opacity = 1, 
            title = "Problem",
            values = ~problem)

16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)


mpls_all <- mpls_nbhd %>% 
  left_join(mpls_suspicious,
            by = c("BDNAME" = "neighborhood")) %>% 
  left_join(MplsDemo,
            by = c("BDNAME" = "neighborhood"))

17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.

pal_mpls_all <- colorNumeric("Blues", domain = mpls_all$proportion_sus) 

leaflet(mpls_all) %>% 
  addTiles() %>% 
  addPolygons(label = ~BDNAME, 
              color = ~pal_mpls_all(proportion_sus),
              weight = 2,
              fillOpacity = 0.9,
              highlight = highlightOptions(color = "block",
                                          fillOpacity = .9,
                                          bringToFront = FALSE)) %>% 
  addLegend(pal = pal_mpls_all, 
            position = "bottomright",
            values = ~proportion_sus, 
            opacity = 0.5, 
            title = "Proportion of suspiciousstops by police")

In this map we see that the proportion of suspicious stops by police is above about 0.7 in the south west areas of Minnaespolis particularly around Morris park, Kweewaydin and Wenonah. There are other localised nodes with a high proportion of stops at other peripheral neighbourhoods such as Armatage in the South west, and Bohanan and shingle creek in the north west. The central-north eastern part of Minneapolis has a very low proportion of suspicious stops - between 0.2 and 0.4.There is no data for South uptown neighbourhood.

18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.

pal_mpls_new <- colorNumeric("Blues", domain = mpls_all$foreignBorn)

leaflet(mpls_all) %>% 
  addTiles() %>% 
  addPolygons(label = ~BDNAME,
              fillOpacity = 0.9,
              color = ~pal_mpls_new(foreignBorn),
              weight = 2,
              highlight = highlightOptions(color = "black", 
                                           fillOpacity = 20,
                                           bringToFront = FALSE)) %>% 
  addLegend(pal = pal_mpls_new,
            values = ~foreignBorn,
            position = "bottomright",
            opacity = 0.5,
            title = "Proportion of Foreign Born")

As an international student I am interested in seeing the proportion of foreign born residents in particular neighborhoods. In this map we see that the Cedar Riverside and East Phillips neighborhoods have more than 40% of their population made up of foreign born residents. This makes sense as there is a high number of east african migrants that live in this area. I find it interesting too that the foreign borns are not spreadout through Minneapolis, but rather life close to eachother to retain a sense of community. Their central location could also be a result of intentional housing policies that house migrants in these areas.

GitHub link

19. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 04_exercises.Rmd, provide a link to the 04_exercises.md file, which is the one that will be most readable on GitHub.

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?